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The 5th International Workshop on Mining and Learning with Graphs

Data Mining and Machine Learning are in the midst of a "structured
revolution". After many decades of focusing on independent and
identically-distributed (iid) examples, many researchers are now
studying problems in which examples consist of collections of
inter-related entities or are linked together into complex graphs. A
major driving force is the explosive growth in the amount of
heterogeneous data that is being collected in the business and
scientific world. Example domains include bioinformatics,
chemoinformatics, transportation systems, communication networks,
social network analysis, link analysis, robotics, among others.
The structures encountered can be as simple as sequences and trees
(such as those arising in protein secondary structure prediction and
natural language parsing) or as complex as citation graphs, the World
Wide Web, and even relational data bases. In all these cases,
structured representations can give a more informative view of the
problem at hand, which is often crucial for the development of
successful mining and learning algorithms.

There have been several workshops on mining and learning from
graphs in recent years such as last year's MLG and its forerunner MGTS
workshop series on Mining Graphs, Trees and Sequences. These were
successful, but were tied to the conference of one research
community. Nowadays there seems to be a surge of interest in mining
and learning from structured data across several communities. Most
researchers, however, only have exposure to one or two communities,
and no clear understanding of the relative advantages and limitations
of different approaches has yet emerged. We believe this is an ideal
time for a workshop that allows active researchers in this area to
discuss and debate the unique challenges of mining and learning from
structured data.
The MLG 2007 workshop will thus
concentrate on mining and learning with structured data in general and
its many appearances and facets such as interpretations, graphs,
trees, sequences. Specifically, we seek to invite researchers in
Statistical Relational Learning, Kernel Methods for Structured
Inputs/Outputs, Graph Mining, (Multi-) Relational Data Mining,
Inductive Logic Programming, among others.